Reasoning With Bias

In recent years, the problem of evaluating the trustworthiness of machine learning systems has become more urgent than ever. A directly related issue is that of assessing the fairness of their decisions. In this work, we adopt a primarily logical perspective on the topic, by trying to highlight the basic logical characteristics of the inferential setting in which a biased prediction occurs. To do so, we first identify and formalise four key desiderata for a logic capable of modelling the behaviour of a biased system, namely: skewness, dependency on data and model, non-monotonicity, and the existence of a minimal distinction between types of bias. On this basis, we define two metrics, one for group and one for individual fairness